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Statistical Optimality and Computational Efficiency of Nyström Kernel PCA
Various approximation schemes have been proposed in the literature to alleviate these computational issues, and the approximate kernel machines are shown to retain the empirical performance.
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
In this paper, we propose and study a Nystr\"om based approach to efficient large scale kernel principal component analysis (PCA).
Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off
We show that the approximate KPCA is both computationally and statistically efficient compared to KPCA in terms of the error associated with reconstructing a kernel function based on its projection onto the corresponding eigenspaces.
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